Clarification of the PCP evaluation criterionThe matlab code to evaluate PCP provided with this dataset represents the official evaluation protocol for the following datasets: Buffy Stickmen, ETHZ PASCAL Stickmen, We Are Family Stickmen, Synchronic Activities Stickmen. In our PCP implementation, a body part produced by an algorithm is considered correctly localized if its endpoints are closer to their ground-truth locations than a threshold (on average over the two endpoints). Using it ensures results comparable to the vast majority of results previously reported on these datasets.
Recently an alternative implementation of the PCP criterion, based on a stricter interpretation of its description in Ferrari et al CVPR 2008 has been used in some works, including Johnson et al. BMVC 2010 and Pishchulin et al CVPR 2012. In this implementation, a body part is considered correct only if both of its endpoints are closer to their ground-truth locations than a threshold. These two different PCP measures are the consequence of the ambiguous wording in the original verbal description of PCP in Ferrari et al CVPR 2008 (which did not mention averaging over endpoints). Importantly, the stricter PCP version has essentially been used only on other datasets than the ones mentioned above, and in particular on IIP (Iterative Image Parsing dataset, Ramanan NIPS 2006) and LSP (Leeds Sports Pose dataset, Johnson et al. BMVC 2010).
In order to keep a healthy research environment and guarantee the comparability of results across different research groups and different years, we recommend the following policy:
- use our evaluation code, which computes the original, looser PCP measure, on Buffy Stickmen, ETHZ PASCAL Stickmen, We Are Family Stickmen, Synchronic Activities Stickmen, i.e. essentially on all datasets released by us
- some other datasets unfortunately have no official evaluation code released with them, and therefore it is harder to establish an exact and fully official protocol. Nonetheless, based on the protocols followed by most papers that appeared so far, we recommend using the strict PCP measure on IIP and LSP. A precise definition of the strict PCP measure can be found in Pishchulin et al CVPR 2012.
D. Ramanan. "Learning to Parse Images of Articulated Objects", In NIPS, 2006.
S. Johnson and M. Everingham "Clustered Pose and Nonlinear Appearance Models for Human Pose Estimation", In BMVC, 2010
L. Pishchulin, A. Jain, M. Andriluka, T. Thormaehlen and B. Schiele "Articulated People Detection and Pose Estimation: Reshaping the Future", In CVPR, 2012